Search results for: interval training

Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

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Authors: Tathagatha Khan, Smruti Sourava Mohapatra

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Median openings are provided in raised median of multilane roads to facilitate U-turn movement. The U-turn movement is a highly complex and risky maneuver because U-turning vehicle (minor stream) makes 180° turns at median openings and merge with the approaching through traffic (major stream). A U-turning vehicle requires a suitable gap in the major stream to merge, and during this process, the possibility of merging conflict develops. Therefore, these median openings are potential hot spot of conflict and posses concern pertaining to safety. The traffic at the median openings could be managed efficiently with enhanced safety when the capacity of a traffic facility has been estimated correctly. The capacity of U-turns at median openings is estimated by Harder’s formula, which requires three basic parameters namely critical gap, follow up time and conflict flow rate. The estimation of conflicting flow rate under mixed traffic condition is very much complicated due to absence of lane discipline and discourteous behavior of the drivers. The understanding of placement of major stream vehicles at median opening is very much important for the estimation of conflicting traffic faced by U-turning movement. The placement data of major stream vehicles at different section in 4-lane and 6-lane divided multilane roads were collected. All the test sections were free from the effect of intersection, bus stop, parked vehicles, curvature, pedestrian movements or any other side friction. For the purpose of analysis, all the vehicles were divided into 6 categories such as motorized 2W, autorickshaw (3-W), small car, big car, light commercial vehicle, and heavy vehicle. For the collection of placement data of major stream vehicles, the entire road width was divided into sections of 25 cm each and these were numbered seriatim from the pavement edge (curbside) to the end of the road. The placement major stream vehicle crossing the reference line was recorded by video graphic technique on various weekdays. The collected data for individual category of vehicles at all the test sections were converted into a frequency table with a class interval of 25 cm each and the placement frequency curve. Separate distribution fittings were tried for 4- lane and 6-lane divided roads. The variation of major stream traffic volume on the placement characteristics of major stream vehicles has also been explored. The findings of this study will be helpful to determine the conflict volume at the median openings. So, the present work holds significance in traffic planning, operation and design to alleviate the bottleneck, prospect of collision and delay at median opening in general and at median opening in developing countries in particular.

Keywords: median opening, U-turn, conflicting traffic, placement, mixed traffic

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Authors: Moyosola Jude Akomolafe

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Career commitment among employees is crucial to the success of any organization. However, career commitment has been reported to be very low among teachers in the public secondary schools in Nigeria. This study, therefore, examined the contributions of emotional intelligence and general self-efficacy to career commitment of among secondary school teachers in Nigeria. Descriptive research design of correlational type was adopted for the study. It made use of stratified random sampling technique was used in selecting two hundred and fifty (250) secondary schools teachers for the study. Three standardized instruments namely: The Big Five Inventory (BFI), Emotional Intelligence Scale (EIS), General Self-Efficacy Scale (GSES) and Career Commitment Scale (CCS) were adopted for the study. Three hypotheses were tested at 0.05 level of significance. Data collected were analyzed through Multiple Regression Analysis to investigate the predicting capacity of emotional intelligence and general self-efficacy on career commitment of secondary school teachers. The results showed that the variables when taken as a whole significantly predicted career commitment among secondary school teachers. The relative contribution of each variable revealed that emotional intelligence and general self-efficacy significantly predicted career commitment among secondary school teachers in Nigeria. The researcher recommended that secondary school teachers should be exposed to emotional intelligence and self-efficacy training to enhance their career commitment.

Keywords: career commitment, emotional intelligence, general self-efficacy, secondary school teachers

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Authors: Omer Abdalla Ishag, Magdi Baker Amien

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Physicians used the stethoscope for listening patient heart sounds in order to make a diagnosis. However, the determination of heart conditions by acoustic stethoscope is a difficult task so it requires special training of medical staff. This study developed an accurate model for analyzing the phonocardiograph signal based on PC and DSP processor. The system has been realized into two phases; offline and real time phase. In offline phase, 30 cases of heart sounds files were collected from medical students and doctor's world website. For experimental phase (real time), an electronic stethoscope has been designed, implemented and recorded signals from 30 volunteers, 17 were normal cases and 13 were various pathologies cases, these acquired 30 signals were preprocessed using an adaptive filter to remove lung sounds. The background noise has been removed from both offline and real data, using wavelet transform, then graphical and statistics features vector elements were extracted, finally a look-up table was used for classification heart sounds cases. The obtained results of the implemented system showed accuracy of 90%, 80% and sensitivity of 87.5%, 82.4% for offline data, and real data respectively. The whole system has been designed on TMS320VC5509a DSP Platform.

Keywords: code composer studio, heart sounds, phonocardiograph, wavelet transform

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Authors: Tahir Munir Butt, Gao Qijie, Babar Shahbaz, Muhammad Zakaria Yousaf Hassan, Zhnag Chuanhong

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The main objective of this paper is to identify the relationship between Information Communication Technology (ICTs) applications with Agricultural development in the process of communication at rural Punjab-Pakistan. The authors analyzed the relationship of ICTs applications with the most prominent factor for the Agricultural Information Services (AIS) in the Agricultural Extension Approaches (AEA). The data collection procedure was started from Jan. 2015 and completed in July 2015. It is the one of the part in PhD studies at China Agriculture, University Hadian-Beijng China. It was observed that on major constraint in the AIS disseminated was the limited number of farmers especially and unknown the farmers about new ICTs technology for Agriculture at rural areas. Majority of ICTs application e.g. Toll free number; Robo Calls; Text message was highly significances in the AIS approach. The recommendation is communication and capacity building one of the indispensable elements for sustainable and agricultural development and Agricultural extension should be provided training to farmer about new ICTs technologies to access and use of it for Sustainable Agriculture Development (SAD) and update the scenario of flow of information also with try to established ICTs hub at the village level.

Keywords: ICTs, AEA, AIS, SAD, rural farmers

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Authors: Mohammed Jamilu Haruna, Kawu Adamu Sule

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This paper examines the evolution of modern policing in Nigeria to the present day, with a focus on the newly introduced community policing, which seeks to cement the operational vacuum created by the repressive and oppressive approach of the Nigeria Police Force (NPF), which renders the police incapable of addressing the twin problems of crime and disorder. Thus, the primary purpose for the implementation of community policing was to use it as a mechanism for building the lost trust between the police and the public, perhaps due to the long history of antagonistic and repressive relationships between them. If properly implemented, community policing has the prospect of empowering Nigerian citizens with the skills to protect themselves against invaders of their private security so that crimes can be prevented before anyone is victimized. Other prospects include, but are not limited to, (i) a favorable public view of the police, (ii) building of mutual trust, (iii) increased information flow through effective communication between the police and the public, and above all, (iv) increased police accountability. Unfortunately, problems such as aged suspicious and distrustful relationships, inadequate funding, poor training of officers, poor monitoring and evaluation of the community policing project, lack of public awareness of the benefits of the program, and sabotage by some of the personnel of the police who benefits from the status quo, were some of the reasons that troubled the implementation of community policing.

Keywords: community, policing, problems, prospects, problem solving

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Authors: Murtala Aminu, Salisu Adamu Aliero, Adamu Muhammed

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Records are a vital asset in ensuring that the institution is governed effectively and efficiently, and is accountable to its staff, students and the community that it serves. The major purpose of this study was to assess record management of the registry department of Kebbi state University of science and technology Aliero. To be able to achieve this objective, research questions were formulated and answers obtained, which centered on records creation, record management policy, challenges facing records management. The review of related literature revealed that there is need for records to be properly managed and in doing so there is need for good records management policy that clearly spells out the various programs required for effective records management. Survey research method was used involving questionnaire, and observation. The findings revealed that the registry department of the University still has a long way to go with respect to day-today records management. The study recommended provision for adequate, modern, safe and functional storage facilities, sufficient and regular funding, recruitment of trained personnel, on the job training for existing staff, computerization of all units records, and uninterrupted power supply to all parts of the unit as a means of ensuring proper records management.

Keywords: records, management, records management policy, registry

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Authors: Mumtaz Mooncey, Alexander Jolly, Megan Fisher, Kerry Robinson, Robert Lloyd, Dave Fielding

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Introduction: The integration of the WingFactors in-situ simulation programme has transformed the education landscape at the Whittington Health NHS Trust. To date, there have been a total of 90 simulations - 19 aimed at Paediatric trainees, including 2 Child and Adolescent Mental Health (CAMHS) scenarios. The opportunity for joint debriefs provided by clinical faculty and airline pilots, has created a new exciting avenue to explore human factors within psychiatry. Through the use of real clinical environments and primed actors; the benefits of high fidelity simulation, interdisciplinary and interprofessional learning has been highlighted. The use of in-situ simulation within Psychiatry is a newly emerging concept and its success here has been recognised by unanimously positive feedback from participants and acknowledgement through nomination for the Health Service Journal (HSJ) Award (Best Education Programme 2021). Methodology: The first CAMHS simulation featured a collapsed patient in the toilet with a ligature tied around her neck, accompanied by a distressed parent. This required participants to consider:; emergency physical management of the case, alongside helping to contain the mother and maintaining situational awareness when transferring the patient to an appropriate clinical area. The second simulation was based on a 17- year- old girl attempting to leave the ward after presenting with an overdose, posing potential risk to herself. The safe learning environment enabled participants to explore techniques to engage the young person and understand their concerns, and consider the involvement of other members of the multidisciplinary team. The scenarios were followed by an immediate ‘hot’ debrief, combining technical feedback with Human Factors feedback from uniformed airline pilots and clinicians. The importance of psychological safety was paramount, encouraging open and honest contributions from all participants. Key learning points were summarized into written documents and circulated. Findings: The in-situ simulations demonstrated the need for practical changes both in the Emergency Department and on the Paediatric ward. The presence of airline pilots provided a novel way to debrief on Human Factors. The following key themes were identified: -Team-briefing (‘Golden 5 minutes’) - Taking a few moments to establish experience, initial roles and strategies amongst the team can reduce the need for conversations in front of a distressed patient or anxious relative. -Use of checklists / guidelines - Principles associated with checklist usage (control of pace, rigor, team situational awareness), instead of reliance on accurate memory recall when under pressure. -Read-back - Immediate repetition of safety critical instructions (e.g. drug / dosage) to mitigate the risks associated with miscommunication. -Distraction management - Balancing the risk of losing a team member to manage a distressed relative, versus it impacting on the care of the young person. -Task allocation - The value of the implementation of ‘The 5A’s’ (Availability, Address, Allocate, Ask, Advise), for effective task allocation. Conclusion: 100% of participants have requested more simulation training. Involvement of airline pilots has led to a shift in hospital culture, bringing to the forefront the value of Human Factors focused training and multidisciplinary simulation. This has been of significant value in not only physical health, but also mental health simulation.

Keywords: human factors, in-situ simulation, inter-professional, multidisciplinary

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Authors: Malin Isaksson

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Teaching French as a foreign language usually implies teaching French literature, especially in higher education. Training university students in literary reading in a foreign language requires addressing several aspects at the same time: the (foreign) language, the poetic language, the aesthetic aspects of the studied works, and various interpretations of them. A pilot study sought to test a teaching model that would support students in learning to perform competent readings and short analyses of French literary works, in a rather independent manner. This shared practice paper describes the use of a flipped classroom method in two French literature courses, a campus course and an online course, and suggests that the teaching model may provide efficient tools for teaching literary reading and analysis in a foreign language. The teaching model builds on a high level of student activity and focuses on attentive reading, meta-perspectives such as theoretical concepts, individual analyses by students where said concepts are applied, and group discussions of the studied texts and of possible interpretations.

Keywords: attentive reading, flipped classroom, literature in foreign language studies, teaching literature analysis

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Authors: Peter G. Hollis, Kim G. Clarke

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Accurate quantification of the overall volumetric oxygen transfer coefficient (KLa) is ubiquitously measured in bioprocesses by analysing the response of dissolved oxygen (DO) to a step change in the oxygen partial pressure in the sparge gas using a DO probe. Typically, the response lag (τ) of the probe has been ignored in the calculation of KLa when τ is less than the reciprocal KLa, failing which a constant τ has invariably been assumed. These conventions have now been reassessed in the context of multiphase bioprocesses, such as a hydrocarbon-based system. Here, significant variation of τ in response to changes in process conditions has been documented. Experiments were conducted in a 5 L baffled stirred tank bioreactor (New Brunswick) in a simulated hydrocarbon-based bioprocess comprising a C14-20 alkane-aqueous dispersion with suspended non-viable Saccharomyces cerevisiae solids. DO was measured with a polarographic DO probe fitted with a Teflon membrane (Mettler Toledo). The DO concentration response to a step change in the sparge gas oxygen partial pressure was recorded, from which KLa was calculated using a first order model (without incorporation of τ) and a second order model (incorporating τ). τ was determined as the time taken to reach 63.2% of the saturation DO after the probe was transferred from a nitrogen saturated vessel to an oxygen saturated bioreactor and is represented as the inverse of the probe constant (KP). The relative effects of the process parameters on KP were quantified using a central composite design with factor levels typical of hydrocarbon bioprocesses, namely 1-10 g/L yeast, 2-20 vol% alkane and 450-1000 rpm. A response surface was fitted to the empirical data, while ANOVA was used to determine the significance of the effects with a 95% confidence interval. KP varied with changes in the system parameters with the impact of solid loading statistically significant at the 95% confidence level. Increased solid loading reduced KP consistently, an effect which was magnified at high alkane concentrations, with a minimum KP of 0.024 s-1 observed at the highest solids loading of 10 g/L. This KP was 2.8 fold lower that the maximum of 0.0661 s-1 recorded at 1 g/L solids, demonstrating a substantial increase in τ from 15.1 s to 41.6 s as a result of differing process conditions. Importantly, exclusion of KP in the calculation of KLa was shown to under-predict KLa for all process conditions, with an error up to 50% at the highest KLa values. Accurate quantification of KLa, and therefore KP, has far-reaching impact on industrial bioprocesses to ensure these systems are not transport limited during scale-up and operation. This study has shown the incorporation of τ to be essential to ensure KLa measurement accuracy in multiphase bioprocesses. Moreover, since τ has been conclusively shown to vary significantly with process conditions, it has also been shown that it is essential for τ to be determined individually for each set of process conditions.

Keywords: effect of process conditions, measuring oxygen transfer coefficients, multiphase bioprocesses, oxygen probe response lag

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Authors: Bharti Singh, Shri K. Singh

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Child malnutrition is a multifaceted issue that transcends geographical boundaries. Malnutrition not only stunts physical growth but also leads to a spectrum of morbidities and child mortality. It is one of the leading causes of death (~50 %) among children under age five. Despite economic progress and advancements in healthcare, child malnutrition remains a formidable challenge for India. The objective is to investigate the impact of women's empowerment on child nutrition outcomes in India from 2006 to 2021. A composite index of women's empowerment was constructed using Confirmatory Factor Analysis (CFA), a rigorous technique that validates the measurement model by assessing how well-observed variables represent latent constructs. This approach ensures the reliability and validity of the empowerment index. Secondly, kernel density plots were utilised to visualise the distribution of key nutritional indicators, such as stunting, wasting, and overweight. These plots offer insights into the shape and spread of data distributions, aiding in understanding the prevalence and severity of malnutrition. Thirdly, linear polynomial graphs were employed to analyse how nutritional parameters evolved with the child's age. This technique enables the visualisation of trends and patterns over time, allowing for a deeper understanding of nutritional dynamics during different stages of childhood. Lastly, multilevel analysis was conducted to identify vulnerable levels, including State-level, PSU-level, and household-level factors impacting undernutrition. This approach accounts for hierarchical data structures and allows for the examination of factors at multiple levels, providing a comprehensive understanding of the determinants of child malnutrition. Overall, the utilisation of these statistical methodologies enhances the transparency and replicability of the study by providing clear and robust analytical frameworks for data analysis and interpretation. Our study reveals that NFHS-4 and NFHS-5 exhibit an equal density of severely stunted cases. NFHS-5 indicates a limited decline in wasting among children aged five, while the density of severely wasted children remains consistent across NFHS-3, 4, and 5. In 2019-21, women with higher empowerment had a lower risk of their children being undernourished (Regression coefficient= -0.10***; Confidence Interval [-0.18, -0.04]). Gender dynamics also play a significant role, with male children exhibiting a higher susceptibility to undernourishment. Multilevel analysis suggests household-level vulnerability (intra-class correlation=0.21), highlighting the need to address child undernutrition at the household level.

Keywords: child nutrition, India, NFHS, women’s empowerment

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Authors: Cheikh Bamba Dione, Alla Lo, Elhadji Mamadou Nguer, Siley O. Ba

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In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and transformer architectures. We trained our models on a parallel French-Wolof corpus of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, back translation, and the copied corpus method. We evaluate the models using the BLEU score and find that transformer outperforms the classic seq2seq model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on word-level-based units. For subword-level models, using back translation proves to be slightly beneficial in low-resource (WO) to high-resource (FR) language translation for the transformer (but not for the seq2seq) models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with back translation leads to a substantial improvement of the translation quality.

Keywords: backtranslation, low-resource language, neural machine translation, sequence-to-sequence, transformer, Wolof

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Authors: Wen-Chao Tang, Tang-Yi Liu, Ming Gao, Gang Xu, Hua-Yuan Yang

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This study developed a method for teaching acupuncture manipulation in small groups optimized with the theory of motor learning. Sixty acupuncture students and their teacher participated in our research. Motion videos were recorded of their manipulations using the lifting-thrusting method. These videos were analyzed using Simi Motion software to acquire the movement parameters of the thumb tip. The parameter velocity curves along Y axis was used to generate small teaching groups clustered by a self-organized map (SOM) and K-means. Ten groups were generated. All the targeted instruction based on the comparative results groups as well as the videos of teacher and student was provided to the members of each group respectively. According to the theory and research of motor learning, the factors or technologies such as video instruction, observational learning, external focus and summary feedback were integrated into this teaching method. Such efforts were desired to improve and enhance the effectiveness of current acupuncture teaching methods in limited classroom teaching time and extracurricular training.

Keywords: acupuncture, group teaching, video instruction, observational learning, external focus, summary feedback

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Authors: Ming-Yen Chang, Sheng-Hung Ke

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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.

Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Jin Woong Lee, Kyuman Cho, Taehoon Kim

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The application of constructability is increasingly required not only in the construction phase but also in the whole project stage. In particular, the proper application of construction experience and knowledge during the design phase enables the minimization of inefficiencies such as design changes and improvements in constructability during the construction phase. In order to apply knowledge effectively, engineering technology efforts should be implemented with design progress. Among many engineering technologies, engineering for temporary works, including facilities, equipment, and other related construction methods, is important to improve constructability. Therefore, as basic research, this study investigates the applicability of temporary work engineering during the design phase in the building construction industry. As a result, application of temporary work engineering has a greater impact on construction cost reduction and constructability improvement. In contrast to the existing design-bid-build method, the turn-key and CM (construct management) procurement methods currently being implemented in Korea are expected to have a significant impact on the direction of temporary work engineering. To introduce temporary work engineering, expert/professional organization training is first required, and a lack of client awareness should be preferentially improved. The results of this study are expected to be useful as reference material for the development of more effective temporary work engineering tasks and work processes in the future.

Keywords: Temporary Work Engineering, Design Phase, Constructability, Building Construction

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Authors: Pintu Modak

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The core strength is essential not only for athletes but also for everyone to perform everyday's household chores with ease and efficiency. Core strength means to strengthen the muscles deep within the abdomen which connect to the spine and pelvis which control the position and movement of the central portion of the body. Strengthening of core muscles is important for injury prevention (lower back pain) and performance enhancement in sports. The purpose of the study was to review the literature and findings on the effects of Yoga exercise as a part of sports training method and fitness programs. Fifteen papers were found to be relevant for this review. There are five simple yoga poses: Ardha Phalakasana (Low plank), Vasisthasana (side plank), Purvottanasana (inclined plane), Sarvangasana (shoulder stand), and Virabhadrasana (Warrior) are found to be very effective for strengthening core muscles. They are the most effective poses to build core strength and flexibility to the core muscles. The study suggests that sports and fitness trainers should include these yoga exercises in their programs to strengthen core muscles.

Keywords: core strength, yoga, injuries, lower back

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Authors: Andrey A. Lipovskii, Alexey V. Redkov, Vyacheslav V. Rusan, Dmitry K. Tagantsev, Valentina V. Zhurikhina

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The presented research is related to the development of recently proposed technique of the formation of composite materials, like optical glass-ceramics, with predetermined structure and properties of the crystalline component. The technique is based on the control of the size and concentration of the crystalline grains using the phenomenon of glass-ceramics decrystallization (vitrification) induced by ion-exchange. This phenomenon was discovered and explained in the beginning of the 2000s, while related theoretical description was given in 2016 only. In general, the developed theory enables one to model the process and optimize the conditions of ion-exchange processing of glass-ceramics, which provide given properties of crystalline component, in particular, profile of the average size of the crystalline grains. The optimization is possible if one knows two dimensionless parameters of the theoretical model. One of them (β) is the value which is directly related to the solubility of crystalline component of the glass-ceramics in the glass matrix, and another (γ) is equal to the ratio of characteristic times of ion-exchange diffusion and crystalline grain dissolution. The presented study is dedicated to the development of experimental technique and simulation which allow determining these parameters. It is shown that these parameters can be deduced from the data on the space distributions of diffusant concentrations and average size of crystalline grains in the glass-ceramics samples subjected to ion-exchange treatment. Measurements at least at two temperatures and two processing times at each temperature are necessary. The composite material used was a silica-based glass-ceramics with crystalline grains of Li2OSiO2. Cubical samples of the glass-ceramics (6x6x6 mm3) underwent the ion exchange process in NaNO3 salt melt at 520 oC (for 16 and 48 h), 540 oC (for 8 and 24 h), 560 oC (for 4 and 12 h), and 580 oC (for 2 and 8 h). The ion exchange processing resulted in the glass-ceramics vitrification in the subsurface layers where ion-exchange diffusion took place. Slabs about 1 mm thick were cut from the central part of the samples and their big facets were polished. These slabs were used to find profiles of diffusant concentrations and average size of the crystalline grains. The concentration profiles were determined from refractive index profiles measured with Max-Zender interferometer, and profiles of the average size of the crystalline grains were determined with micro-Raman spectroscopy. Numerical simulation were based on the developed theoretical model of the glass-ceramics decrystallization induced by ion exchange. The simulation of the processes was carried out for different values of β and γ parameters under all above-mentioned ion exchange conditions. As a result, the temperature dependences of the parameters, which provided a reliable coincidence of the simulation and experimental data, were found. This ensured the adequate modeling of the process of the glass-ceramics decrystallization in 520-580 oC temperature interval. Developed approach provides a powerful tool for fine tuning of the glass-ceramics structure, namely, concentration and average size of crystalline grains.

Keywords: diffusion, glass-ceramics, ion exchange, vitrification

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Endale Fantahun Tadesse, Sabika Khalid

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The global call for increased male representation in early childhood education (ECE) has garnered significant attention. Emerging studies have indicated that involving men in ECE can yield positive outcomes for children's physical and psychological development. Challenging the prevailing misconception and stereotype that women dominate the ECE sector is crucial. In light of this, the present study undertakes a systematic review of nine studies on males working in ECE, revealing a dearth of male presence in the field in China as well. To address this issue, substantial structural changes must be implemented to enhance the inadequate pay and working conditions that dissuade both men and women from pursuing a sustainable career in ECE. It is recommended that school leadership raise awareness among female teachers and parents, encouraging them to support and uphold virtuous values for male teachers. Additionally, governing bodies should provide explicit guidelines during training programs to address concerns regarding potential abuse and gender biases. The findings of this review underscore the need for future studies to examine the self-identities of male teachers from various stakeholders' perspectives and explore the consequences of being in the profession through rigorous and robust methodologies that can inform policymakers.

Keywords: male teachers, Early Childhood Education (ECE), self-identity, perception of stakeholders

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Authors: Mohammad Mahmoud Rjoob

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This study aimed to explore the impact of democratic leadership on job satisfaction among teachers in the South Hebron Directorate schools. The study was applied to a random sample representing the study population of teachers in the South Hebron Directorate of Education, with a sample size of 301 teachers from 12 schools. The researcher adopted the descriptive approach as it is the most suitable for the nature of this study, and a questionnaire was used as a tool for data collection and measuring various variables. The study recommended the importance of enhancing the concept of democratic leadership in schools to boost teachers' morale and improve the quality of the educational process. It also encouraged the adoption of democratic leadership styles by administrations, educational areas, and new principals due to their positive and effective impact on job performance. Additionally, the study suggested providing training courses for school principals and new teachers on how to apply the principles of democratic leadership that contribute to creating a positive educational environment and enhance the spirit of cooperation to achieve the school's goals. Finally, the study called for granting school principals more authority and powers to increase their ability to effectively deal with challenges and problems, which contributes to improving the educational process and enhances teachers' job satisfaction.

Keywords: democratic leadership, job satisfaction, teachers, South Hebron Directorate Schools

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, prior distribution and approximate posterior distribution, KTH dataset

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Authors: Rohith J. K., Amir Nazemi, Abbas S. Milani

Abstract:

In Paralympic sports, athletes often depend on some form of equipment to enable competitive sporting, where most of this equipment would only allow passive physiological supports and discrete physiological measurements. Active feedback physiological support and continuous detection of performance indicators, without time or space constraints, would be beneficial in more effective training and performance measures of Paralympic athletes. Moreover, occasionally the athletes suffer from fatigue and muscular stains due to improper monitoring systems. The latter challenges can be overcome by using Smart Composites technology when manufacturing, e.g., knee brace and other sports wearables utilities, where the sensors can be fused together into the fabric and an assisted system actively support the athlete. This paper shows how different sensing functionality may be created by intrinsic and extrinsic modifications onto different types of composite fabrics, depending on the level of integration and the employed functional elements. Results demonstrate that fabric sensors can be well-tailored to measure muscular strain and be used in the fabrication of a smart knee brace as a sample potential application. Materials, connectors, fabric circuits, interconnects, encapsulation and fabrication methods associated with such smart fabric technologies prove to be customizable and versatile.

Keywords: smart composites, sensors, smart fabrics, knee brace

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

Abstract:

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

Abstract:

Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Priyadarsini Samal, Rajesh Singla

Abstract:

Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

Abstract:

Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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